Improving Breast Cancer Diagnosis through Advanced Image Analysis and Neural Network Classifications

Abstract

Breast cancer, one of the deadly cancers affecting women globally, is caused by genetic mutations that affect cell growth and division. Limitations are present in traditional recognition methods such as mammography, image fusion, and convolutional neural networks (CNNs) in accurately distinguishing between benign, malignant, and normal breast tissues. An enhanced recognition system utilizing advanced image analysis techniques and neural network classifications is proposed by this research to improve diagnostic accuracy. A combination of grey level co-occurrence matrix (GLCM) for texture feature extraction, Expectation Maximization based Gaussian Mixture Model (EMGMM) segmentation, and K-means clustering algorithms are employed by the proposed system. Robust analysis and classification of breast tissue images are provided by the integration of these methods, offering a more precise differentiation between benign, malignant, and normal conditions. The reduction of errors associated with traditional screening methods, enhanced noise recognition, and a non-invasive approach compared to conventional biopsy techniques are included among the key benefits of this system. Improved precision and reliability in breast cancer diagnosis are achieved through the use of neural networks combined with advanced image analysis algorithms. The time and discomfort associated with traditional diagnostic procedures are also reduced by this system, making it a more user-friendly option for patients. The early recognition and treatment of breast cancer are aimed to be enhanced by leveraging these advanced techniques, contributing to lower mortality rates. The potential of integrating GLCM, EMGMM segmentation, and K-means clustering with neural networks, providing a more effective solution for breast cancer screening and diagnosis. Significant improvements in the efficiency of breast cancer diagnostics are promised by this innovative approach, achieving 96.8% accuracy.